Many process facilities (such as manufacturing plants or crude oil refineries) are often managed using distributed control systems. Contemporary control systems often include numerous models tailored to control or monitor various processes associated with the facilities. Conventional systems link these modules together to produce the distributed nature of the control systems. This provides increased performance and the ability to expand or reduce the control system to satisfy changing facility needs.
Some control systems can be tailored to satisfy wide ranges of process requirements (such as global, local, or otherwise) and facility types (such as manufacturing or refining). These types of control systems are often designed with two principal objectives. One is to centralize control of as many processes as possible to improve the overall efficiency of a facility. Another is to support a common interface that communicates data among various modules controlling or monitoring the processes and also with any centralized controller or operator center.
Each process or group of associated processes being controlled often has certain input characteristics (such as flow, feed, or power) and certain output characteristics (such as temperature or pressure). In recent years, model predictive control (“MPC”) techniques have been used to optimize certain processes as a function of such characteristics. One MPC technique uses algorithmic representations of certain processes to estimate characteristic values (often referred to as “controlled variables” represented as parameters, process variables, or estimated observations) associated with the processes in order to better control such processes. In recent years, physical, economic, and other factors have been incorporated into control systems for the associated processes.
Some process facility control systems have been designed to handle operations in a process facility that has a continuous supply chain. A continuous supply chain is one in which the inputs to the process (such as feed stocks or fuel supplies) are continuously supplied to the process. An example of an industrial plant that employs a continuous supply chain is an oil refinery. However, many manufacturing systems do not employ continuous supply chains but instead employ discrete supply chains. A discrete supply chain is one in which the inputs to a process are not continuously supplied to the process. An example of an industrial manufacturing process that employs a discrete supply chain is a semiconductor manufacturing process. Discrete supply chain processes are often systems of processes at different locations (sometimes globally dispersed), such as those used to accomplish the global manufacturing and distribution of associated products.
A semiconductor manufacturing process may produce semiconductor devices by building components such as transistors, interconnecting the components, and testing the components for initial functionality. Many processing steps, such as hundreds of steps, may be carried out in the manufacturing process. The manufacturing steps are highly re-entrant and may employ many different machine types over a lengthy period of time, such as dozens of different machine types used over a number of months.
In the assembly portion of the semiconductor manufacturing process, the semiconductor devices are then mounted in packages and tested for final functionality. This may involve many packaging steps and many different machine types over a lengthy period of time, such as dozens of packaging steps performed on twenty different machine types over a period of one or two weeks. Finally, the devices are configured, marked, packed, and shipped to customers. This could involve the performance of ten or more steps on ten or more machine types over a period of several days.
A manufacturing process that employs a discrete supply chain provides different complexities when compared to a manufacturing process that employs a continuous supply chain. For example, in a discrete supply chain, system dynamics can be complicated by highly re-entrant process flows. There is also a temporal randomness to the discrete supply chain. That is, the amount of time between steps may vary in a random way. There may further be uncertain production yields in the process and uncertain demand from the customers for the products. In addition, there may be a more complex supply network for obtaining the components to manufacture the products. These factors make it very difficult to provide manufacturing control and optimization for a manufacturing process that employs a discrete supply chain.